An OGSA-based integration of life-scientific resources for drug discovery.

OBJECTIVES: The rapid progress of life-scientific research has the potential to dramatically change the paradigm of drug discovery. Efficient utilization of life-scientific resources, i.e., databases and analytic software tools, poses a challenging issue with regard to the reduction of time and cost in the drug discovery process. In this paper, a variety of heterogeneous Web-based life-scientific resources are integrated toward the improvement of drug discovery performance. METHODS: For the integration of heterogeneous life-scientific resources, a database federation technique based on three-layer architecture has been utilized. With the federation technique, life-scientific resources are integrated step by step through database layers, database integration layers and analysis layers to encapsulate complexity and heterogeneity. In this study, we have taken advantage of the latest Grid technology based on OGSA (Open Grid Services Architecture) for the implementation of our approach. RESULTS: The actual case of life-scientific resources for drug discovery demonstrates that our prototype system developed with the proposed technique works well for the identification process of candidate compounds to a target protein. In other words, the prototype system allows a researcher to retrieve candidate compounds with less effort than before. CONCLUSIONS: The usefulness of the prototypic system represents the ability of our approach to integrate heterogeneous life-scientific resources, which have the potential to dramatically improve efficiency in drug discovery, resulting in the shortening of drug development. On the other hand, the system requires further consideration from the aspect of practical use. Dynamic aggregation of the resources is one example of such a consideration.

A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy.

In many of the chemical reactions in living cells, enzymes act as catalysts in the conversion of certain compounds (substrates) into other compounds (products). Comparative analyses of the metabolic pathways formed by such reactions give important information on their evolution and on pharmacological targets (Dandekar et al. 1999). Each of the enzymes that constitute a pathway is classified according to the EC (Enzyme Commission) numbering system, which consists of four sets of numbers that categorize the type of the chemical reaction catalyzed. In this study, we consider that reaction similarities can be expressed by the similarities between EC numbers of the respective enzymes. Therefore, in order to find a common pattern among pathways, it is desirable to be able to use the functional hierarchy of EC numbers to express the reaction similarities. In this paper, we propose a multiple alignment algorithm utilizing information content that is extended to symbols having a hierarchical structure. The effectiveness of our method is demonstrated by applying the method to pathway analyses of sugar, DNA and amino acid metabolisms.